Design a multi-class classification and interpretability pipeline that distinguishes rheumatoid arthritis (RA), psoriatic arthritis (PsA), and osteoarthritis (OA) from hand/feet X-ray images. The architecture combines a CNN backbone (e.g., DenseNet or EfficientNet) for disease classification with an attention-based or Grad-CAM explanation module to

Design 'EfficientDenseNet' — a DenseNet variant under 10M params that incorporates VoVNet-style one-shot aggregation (OSA), depthwise separable bottlenecks, squeeze-and-excitation attention, LayerNorm pre-activation, and aggressive compound scaling. The goal is to train 2-3× faster than DenseNet-121 while maintaining or improving accuracy on ImageN